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基于多层注意力和消息传递网络的药物相互作用预测方法

饶晓洁 张通 孟献兵 陈俊龙

饶晓洁, 张通, 孟献兵, 陈俊龙. 基于多层注意力和消息传递网络的药物相互作用预测方法. 自动化学报, 2022, 48(x): 1−13 doi: 10.16383/j.aas.c220371
引用本文: 饶晓洁, 张通, 孟献兵, 陈俊龙. 基于多层注意力和消息传递网络的药物相互作用预测方法. 自动化学报, 2022, 48(x): 1−13 doi: 10.16383/j.aas.c220371
Rao Xiao-Jie, Zhang Tong, Meng Xian-Bing, Chen C.L.Philip. Drug-drug interaction prediction method based on multi-level attention mechanism and message passing neural network. Acta Automatica Sinica, 2022, 48(x): 1−13 doi: 10.16383/j.aas.c220371
Citation: Rao Xiao-Jie, Zhang Tong, Meng Xian-Bing, Chen C.L.Philip. Drug-drug interaction prediction method based on multi-level attention mechanism and message passing neural network. Acta Automatica Sinica, 2022, 48(x): 1−13 doi: 10.16383/j.aas.c220371

基于多层注意力和消息传递网络的药物相互作用预测方法

doi: 10.16383/j.aas.c220371
基金项目: 国家重点研发计划项目(2019YFB1703600), 国家自然科学基金 (62006081), 中国博士后科学基金项目(2020M672630)资助
详细信息
    作者简介:

    饶晓洁:华南理工大学计算机科学与工程学院硕士研究生. 主要研究方向为机器学习与人工智能在生物医药领域的应用. E-mail: rxj19971214@163.com

    张通:华南理工大学计算机科学与工程学院教授.2016年获得澳门大学软件工程专业博士学位. 主要研究方向为情感计算, 进化计算, 神经网络和其他机器学习技术及其应用. E-mail: tony@scut.edu.cn

    孟献兵:华南理工大学计算机科学与工程学院助理研究员.2019年获中南大学博士学位. 主要研究方向为计算智能和人工智能算法及其应用. 本文通信作者. E-mail: axbmeng@gmail.com

    陈俊龙:华南理工大学计算机科学与工程学院特聘讲席教授及院长. IEEE Fellow, 美国科学促进会AAAS Fellow, IAPR Fellow, 国际系统及控制论科学院IASCYS院士, 香港工程师学会Fellow, 中国自动化学会Fellow. 欧洲科学院 (AE) 外籍院士、欧洲科学与艺术学院 (EASA) 院士, 国际系统与控制论科学院(IASCYS) 院士. 1985年毕业于美国密歇根州安娜堡市的密歇根大学安娜堡分校, 2016年获得由母校普渡大学(1988年获得博士学位) 颁发的杰出电气和计算机工程师奖. 他在2018年获IEEE系统人机控制论的最高学术奖——IEEE诺伯特·维纳奖 (Norbert Wiener Award). 主要研究方向为控制论、系统和计算智能. E-mail: philipchen@scut.edu.cn

Drug-Drug Interaction Prediction Method Based on Multi-Level Attention Mechanism and Message Passing Neural Network

Funds: Supported by National Key development Program of China (2019YFB1703600), National Natural Science Foundation of China (62006081), China Postdoctoral Science Foundation Project(2020M672630)
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    Author Bio:

    RAO Xiao-Jie Master student at the School of Computer Science and Engineering, South China University of Technology. Her main research interests covers the application of machine learning and artificial intelligence in the field of biomedicine

    ZHANG Tong Professor at the School of Computer Science and Engineering, South China University of Technology, China. and the Ph. D. degree in software engineering from the University of Macau, Macau, China in 2016. His research interest covers affective computing, evolutionary computation, neural network, and other machine learning techniques and their applications

    Meng Xian-Bing Assistant research fellow at the School of Computer Science and Engineering, South China University of Technology. He received his Ph. D. degree in 2019 from Central South University. His research interest covers computational intelligence and artificial intelligence algorithms and their applications. Corresponding author of this paper

    CHEN C. L. Philip Chair professor and dean of the College of Computer Science and Engineering, South China University of Technology. He is a Fellow of IEEE, AAAS, IAPR, CAA, and HKIE; a member of Academia Europaea(AE), European Academy of Sciences and Arts (EASA), and International Academy of Systems and Cybernetics Science (IASCYS). Dr. Chen was a recipient of the 2016 Outstanding Electrical and Computer Engineers Award from his alma mater, Purdue University (in 1988), after he graduated from the University of Michigan at Ann Arbor, Ann Arbor, MI, USA in 1985. He received IEEE Norbert Wiener Award in 2018 for his contribution in systems and cybernetics, and machine learnings. His research interest covers cybernetics, systems, and computational intelligence

  • 摘要: 药物相互作用(Drug-drug interaction, DDI)是指不同药物存在抑制或促进等作用. 现有DDI预测方法往往直接利用药物分子特征表示预测DDI, 而忽略药物分子中不同原子对DDI的影响. 为此, 提出基于多层次注意力机制和消息传递神经网络的DDI预测方法. 该方法将DDI建模为通过学习基于序列表示的药物分子特征实现DDI预测的链接预测问题. 首先, 建立基于注意力机制和消息传递神经网络的原子特征网络, 结合提出的基于分子质心的位置编码, 学习不同原子及其相关联化学键的特征, 构建基于图结构的药物分子特征表示; 然后, 设计基于注意力机制的分子特征网络, 并通过监督和对比损失学习, 实现DDI预测; 最后, 通过实验证明该方法的有效性和优越性.
  • 图  1  模型框架图

    Fig.  1  Framework of the proposed model

    图  2  基于注意力机制的消息传递原子特征网络

    Fig.  2  Framework of the atomic feature network

    图  3  药物分子之间注意力分数的可视化

    Fig.  3  Visualization of attention score between drug molecules

    图  4  位置编码对模型收敛性能的影响

    Fig.  4  The effect of positional coding on model convergence

    图  5  在ZhangDDI数据集上不同$\alpha$$\beta$取值对模型性能的影响

    Fig.  5  The effects of different $\alpha$ and $\beta$ on model performance on ZhangDDI dataset

    图  6  在ChCh-Miner数据集上不同$\alpha$$\beta$取值对模型性能的影响

    Fig.  6  The effects of different $\alpha$ and $\beta$ on model performance on ChCh-Miner dataset

    表  1  ZhangDDI数据集上的对比实验结果

    Table  1  Comparison results on ZhangDDI dataset

    模型AUROCAUPRCF1
    NN[24]67.81±0.2552.61±0.2749.84±0.43
    LP-Sub[25]93.39±0.1389.15±0.1379.61±0.16
    LP-SE[25]93.48±0.2589.61±0.1979.83±0.61
    LP-OSE[25]93.50±0.2490.31±0.8280.41±0.51
    MF-Ens[11]95.20±0.1492.51±0.1585.41±0.16
    SSP-MLP[1]92.51±0.1588.51±0.6680.69±0.81
    GCN[26]91.91±0.6288.73±0.8481.61±0.39
    GIN[27]81.45±0.2677.16±0.1664.15±0.16
    Att-auto[12]92.84±0.6190.21±0.1970.96±0.39
    GAT[28]91.49±0.2990.69±0.1080.93±0.25
    SEAL-CI[29]92.93±0.1992.82±0.1784.74±0.17
    NFP-GCN[30]93.22±0.0993.07±0.4685.29±0.38
    MIRACLE[13]98.95±0.1598.17±0.0693.20±0.27
    本文方法99.14±0.0197.97±0.0293.79±0.28
    下载: 导出CSV

    表  2  ChCh-Miner数据集上的对比实验结果

    Table  2  Comparison results on ChCh-Miner dataset

    模型AUROCAUPRCF1
    GCN[26]82.84±0.6184.27±0.6670.54±0.87
    GIN[27]70.32±0.8772.41±0.6365.54±0.97
    GAT[28]85.84±0.2388.14±0.2576.51±0.38
    SEAL-CI[29]90.93±0.1989.38±0.3984.74±0.48
    NFP-GCN[30]92.12±0.0993.07±0.6985.41±0.18
    MIRACLE[13]96.15±0.2995.57±0.1992.26±0.09
    本文方法98.45±0.3199.79±0.0496.51±0.84
    下载: 导出CSV

    表  3  原子特征网络的消融实验结果

    Table  3  Ablation results on atomic feature network

    数据集算法AUROCAUPRCF1
    ZhangDDI无注意力的
    原子网络
    98.70±0.2096.89±0.5090.46±1.18
    本文方法99.14±0.0197.97±0.0293.79±0.28
    ChCh-Miner无注意力的
    原子网络
    95.90±0.99 99.18±0.1596.23±0.34
    本文方法98.45±0.3199.79±0.0496.51±0.84
    下载: 导出CSV

    表  4  分子特征网络的消融实验结果

    Table  4  Ablation results on molecular feature network

    数据集算法AUROCAUPRCF1
    ZhangDDI无注意力的
    分子网络
    98.82±0.2797.18±0.6891.60±1.84
    本文方法99.14±0.0197.97±0.0293.79±0.28
    ChCh-Miner无注意力的
    分子网络
    95.78±1.2999.19±0.3895.19±1.45
    本文方法98.45±0.3199.79±0.0496.51±0.84
    下载: 导出CSV

    表  5  位置编码对模型性能影响的对比结果

    Table  5  Comparison results of the impact of positional coding on model performance

    数据集算法AUROCAUPRCF1
    ZhangDDI无位置编码98.91±0.2697.46±0.6091.38±2.11
    传统位置编码99.02±0.2197.68±0.5392.70±1.35
    本文方法99.14±0.0197.97±0.0293.79±0.28
    ChCh-Miner无位置编码95.62±2.7999.11±0.6396.12±0.58
    传统位置编码97.54±0.4699.66±0.0694.73±0.54
    本文方法98.45±0.3199.79±0.0496.51±0.84
    下载: 导出CSV

    表  6  损失函数对模型性能影响的对比结果

    Table  6  Comparison results of the impact of loss function on model performance

    数据集算法AUROCAUPRCF1
    ZhangDDI无自蒸馏约束项98.71±0.0196.87±0.0289.53±0.54
    无对比学习损失项94.19±0.0679.62±0.3173.59±0.39
    infoNCE对比损失项99.10±0.0597.91±0.0992.87±0.60
    不同采样方式的对比损失项99.13±0.0297.97±0.0493.15±0.61
    本文方法99.14±0.0197.97±0.0293.79±0.28
    ChCh-Miner无自蒸馏约束项97.55±3.2499.48±0.8796.34±1.01
    无对比学习损失项58.7±5.0090.3±1.0694.89±0.57
    infoNCE对比损失项98.59±0.2099.80±0.0397.31±0.20
    不同采样方式的对比损失项98.38±0.0199.78±0.0095.67±0.09
    本文方法98.45±0.3199.79±0.0496.51±0.84
    下载: 导出CSV
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  • 收稿日期:  2022-05-06
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